IEEE Signal Processing - July 2018 - 93

importance of game theory and its extensive use in smartenergy research
2) focusing on basic P2P energy-trading techniques for integrating renewable energy sources into the grid by describing key features of such trading networks and providing a
description of an existing testbed that has deployed P2P
trading for managing energy
3) detailing some specific game- and auction-theoretic models that have been used for P2P energy trading and sharing
some key results arising from those approaches, providing
the reader with an understanding of game theory in the P2P
energy-trading paradigm and its potential benefits.
This article can be used as a reference by both new and
experienced researchers in the emerging field of smart grid
research. Signal processing is part of a much larger smart grid
context, and, in this article, we examine only the applications
of various game- and auction-theoretic approaches for managing the trading between different entities within the energy
network. This article, therefore, seeks to complement existing
game-theoretic literature in guiding engineers to effectively
design and manage the substantial energy generated by DERs

across the overall network without compromising the stability
of the grid and, thereby, to enable prosumers with conflicting
interests to actively engage in energy trading with one another.
Table 1 provides a summary of the pros and cons of game theory in addressing various challenges in energy systems as well
as its similar application in other sectors.

Game theory for smart energy management
Within the context of energy management in smart grids, the
applications of game- and auction-theoretic approaches are
plentiful. On the one hand, noncooperative games have been
used extensively to schedule energy-related activities and,
subsequently, to trade surplus energy with buyers to earn revenue. On the other hand, the recent large-scale integration of
alternative energy sources, e.g., EVs [11], solar arrays [12],
and wind turbines [13], into the grid has exploited game theory
to provide regulatory service for energy trading [14] and efficient home energy management [15]. In this section, we discuss the application of different game- and auction-theoretic
approaches across EV domains, DERs and their storage
domains, and service domains. We first provide a brief

Table 1. The advantages and limitations of using game theory for designing energy-management schemes.
Area

Focus

Brief Discussion

Energy management
without P2P

Challenges

Designing management schemes based on the large amount of data from smart meters; modeling
users' behavior; forecasting users' demand; forecasting the generation from renewable energy
sources; modeling the interaction between the grid and customers; incentive design for increasing
users' participation; improving grid stability in response to extensive renewable integration into
the grid; and accommodation and coordination of EVs in the network.

Advantages of game theory

A well-established, mathematically tractable, and proven optimization technique that can easily
be integrated with other signal processing and data-driven techniques such as machine learning;
is compatible with Internet of Things (IoT) devices; suitably captures the interactive nature of
management problems and includes users' rational behavior in the modeling; and is applicable
to any domain of energy networks including EV, residential, industrial, and commercial.

Limitations of game theory

The practical deployment of game-theoretic models is limited and difficult to implement when it
needs to directly involve human subjects in the optimization process. Furthermore, it is heavily
dependent upon the performance of the communication network, which could potentially limit the
performance of the process in case of a natural calamity or network congestion.

Similar applications in
sectors other than energy

These include financial modeling (allocation of investors' savings among financial assets); behavioral science (rationality of human behavior); computer science (testing boundaries of algorithms);
resource management (water conflicts in a river basin); agriculture (irrigation strategies followed
by stakeholders); and communication systems (channel allocation).

Challenges

These include modeling user behavior; designing pricing schemes that help users to cooperate in
the P2P network; managing the security and privacy of users; establishing strategy-proof transactions; maintaining trust between users without a centralized authority; reducing reliance upon the
central grid, either partially or completely; managing network congestion when the number of
users becomes large; stabilizing the system due to the increased penetration of renewables; and
incorporating the central power station as a part of the P2P trading.

Advantages of game theory

It can model users' behavior and their interactive trading with one another and easily integrate
pricing and incentive design as a part of game framework development; potentially establish the
trust between the users within the network and motivate them to cooperate via its game framework; and be incorporated with other signal processing techniques, such as fuzzy logic and
machine learning.

Limitations of game theory

The practical deployment of game-theoretic models is limited and difficult to implement when it
needs to directly involve human subjects in the optimization process. However, there are recent
applications of game theory (auction game) in pilot P2P projects, such as the BMP.

Similar applications in
sectors other than energy

These include banks (online financial transactions); IoT (device discovery and device control);
health care (P2P help with patients who have chronic conditions); real estate (P2P lending); and
finance (debt financing).

Energy management
in P2P

IEEE Signal Processing Magazine

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July 2018

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Table of Contents for the Digital Edition of IEEE Signal Processing - July 2018

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